



'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 							SCREAMING_SNAKE_CASE(				_lowerCAmelCase				,						unittest.TestCase     ):







	"""simple docstring"""






	lowerCamelCase__     					=			KandinskyVaaControlnetImgaImgPipeline
	lowerCamelCase__     					=			["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
	lowerCamelCase__     					=			["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
	lowerCamelCase__     					=			[
	    """generator""",
	    """height""",
	    """width""",
	    """strength""",
	    """guidance_scale""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	lowerCamelCase__     					=			False







	@property
	def 	A      (		self     :	Dict						)     ->						Dict:
								return 32







	@property
	def 	A      (		self     :	List[str]						)     ->						List[Any]:
								return 32







	@property
	def 	A      (		self     :	int						)     ->						str:
								return self.time_input_dim







	@property
	def 	A      (		self     :	List[str]						)     ->						List[Any]:
								return self.time_input_dim * 4







	@property
	def 	A      (		self     :	Union[str, Any]						)     ->						Optional[Any]:
								return 100







	@property
	def 	A      (		self     :	int						)     ->						Optional[Any]:
								torch.manual_seed(0						)

								UpperCAmelCase					:			Optional[int]									=						{
								    """in_channels""": 8,
								    # Out channels is double in channels because predicts mean and variance
								    """out_channels""": 8,
								    """addition_embed_type""": """image_hint""",
								    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
								    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
								    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
								    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
								    """layers_per_block""": 1,
								    """encoder_hid_dim""": self.text_embedder_hidden_size,
								    """encoder_hid_dim_type""": """image_proj""",
								    """cross_attention_dim""": self.cross_attention_dim,
								    """attention_head_dim""": 4,
								    """resnet_time_scale_shift""": """scale_shift""",
								    """class_embed_type""": None,
								}

								UpperCAmelCase					:			int									=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE_						)
								return model







	@property
	def 	A      (		self     :	List[Any]						)     ->						Optional[Any]:
								return {
								    "block_out_channels": [32, 32, 64, 64],
								    "down_block_types": [
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "AttnDownEncoderBlock2D",
								    ],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "vq_embed_dim": 4,
								}







	@property
	def 	A      (		self     :	Any						)     ->						List[str]:
								torch.manual_seed(0						)
								UpperCAmelCase					:			List[str]									=						VQModel(**self.dummy_movq_kwargs						)
								return model







	def 	A      (		self     :	List[str]						)     ->						str:
								UpperCAmelCase					:			Optional[Any]									=						self.dummy_unet
								UpperCAmelCase					:			Dict									=						self.dummy_movq

								UpperCAmelCase					:			List[str]									=						{
								    """num_train_timesteps""": 1000,
								    """beta_schedule""": """linear""",
								    """beta_start""": 0.0_00_85,
								    """beta_end""": 0.0_12,
								    """clip_sample""": False,
								    """set_alpha_to_one""": False,
								    """steps_offset""": 0,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								}

								UpperCAmelCase					:			int									=						DDIMScheduler(**SCREAMING_SNAKE_CASE_						)

								UpperCAmelCase					:			str									=						{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components







	def 	A      (		self     :	Optional[int]						,     __snake_case     :	int						,     __snake_case     :	Optional[int]=0						)     ->						int:
								UpperCAmelCase					:			str									=						floats_tensor((1, self.text_embedder_hidden_size)						,     rng=random.Random(SCREAMING_SNAKE_CASE_						)						).to(SCREAMING_SNAKE_CASE_						)
								UpperCAmelCase					:			Any									=						floats_tensor((1, self.text_embedder_hidden_size)						,     rng=random.Random(seed + 1						)						).to(
								    SCREAMING_SNAKE_CASE_						)
								# create init_image
								UpperCAmelCase					:			List[Any]									=						floats_tensor((1, 3, 64, 64)						,     rng=random.Random(SCREAMING_SNAKE_CASE_						)						).to(SCREAMING_SNAKE_CASE_						)
								UpperCAmelCase					:			Optional[int]									=						image.cpu().permute(0						,     2						,     3						,     1						)[0]
								UpperCAmelCase					:			str									=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_						)						).convert('''RGB'''						).resize((256, 256)						)
								# create hint
								UpperCAmelCase					:			Tuple									=						floats_tensor((1, 3, 64, 64)						,     rng=random.Random(SCREAMING_SNAKE_CASE_						)						).to(SCREAMING_SNAKE_CASE_						)

								if str(SCREAMING_SNAKE_CASE_						).startswith('''mps'''						):
															UpperCAmelCase					:			Optional[Any]									=						torch.manual_seed(SCREAMING_SNAKE_CASE_						)
								else:
															UpperCAmelCase					:			int									=						torch.Generator(device=SCREAMING_SNAKE_CASE_						).manual_seed(SCREAMING_SNAKE_CASE_						)
								UpperCAmelCase					:			List[Any]									=						{
								    """image""": init_image,
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """hint""": hint,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """num_inference_steps""": 10,
								    """guidance_scale""": 7.0,
								    """strength""": 0.2,
								    """output_type""": """np""",
								}
								return inputs







	def 	A      (		self     :	List[str]						)     ->						Optional[int]:
								UpperCAmelCase					:			List[Any]									=						"""cpu"""

								UpperCAmelCase					:			int									=						self.get_dummy_components()

								UpperCAmelCase					:			Dict									=						self.pipeline_class(**SCREAMING_SNAKE_CASE_						)
								UpperCAmelCase					:			List[str]									=						pipe.to(SCREAMING_SNAKE_CASE_						)

								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_						)

								UpperCAmelCase					:			str									=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_						)						)
								UpperCAmelCase					:			int									=						output.images

								UpperCAmelCase					:			Dict									=						pipe(
								    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_						)						,     return_dict=SCREAMING_SNAKE_CASE_						,     )[0]

								UpperCAmelCase					:			int									=						image[0, -3:, -3:, -1]
								UpperCAmelCase					:			Any									=						image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								UpperCAmelCase					:			Union[str, Any]									=						np.array(
								    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]						)
								assert (
								    np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""

@slow
@require_torch_gpu
class 							SCREAMING_SNAKE_CASE(				unittest.TestCase     ):







	"""simple docstring"""






	def 	A      (		self     :	int						)     ->						int:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def 	A      (		self     :	Dict						)     ->						List[Any]:
								UpperCAmelCase					:			Union[str, Any]									=						load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''						)

								UpperCAmelCase					:			List[Any]									=						load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''						)
								UpperCAmelCase					:			Dict									=						init_image.resize((512, 512)						)

								UpperCAmelCase					:			str									=						load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/hint_image_cat.png'''						)
								UpperCAmelCase					:			Optional[int]									=						torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_						)						).float() / 255.0
								UpperCAmelCase					:			Any									=						hint.permute(2						,     0						,     1						).unsqueeze(0						)

								UpperCAmelCase					:			Optional[int]									=						"""A robot, 4k photo"""

								UpperCAmelCase					:			Optional[int]									=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''						,     torch_dtype=torch.floataa						)
								pipe_prior.to(SCREAMING_SNAKE_CASE_						)

								UpperCAmelCase					:			str									=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''						,     torch_dtype=torch.floataa						)
								UpperCAmelCase					:			List[Any]									=						pipeline.to(SCREAMING_SNAKE_CASE_						)

								pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_						)

								UpperCAmelCase					:			Union[str, Any]									=						torch.Generator(device='''cpu'''						).manual_seed(0						)

								UpperCAmelCase					:			str									=						pipe_prior(
								    SCREAMING_SNAKE_CASE_						,     image=SCREAMING_SNAKE_CASE_						,     strength=0.85						,     generator=SCREAMING_SNAKE_CASE_						,     negative_prompt=''''''						,     ).to_tuple()

								UpperCAmelCase					:			Optional[int]									=						pipeline(
								    image=SCREAMING_SNAKE_CASE_						,     image_embeds=SCREAMING_SNAKE_CASE_						,     negative_image_embeds=SCREAMING_SNAKE_CASE_						,     hint=SCREAMING_SNAKE_CASE_						,     generator=SCREAMING_SNAKE_CASE_						,     num_inference_steps=100						,     height=512						,     width=512						,     strength=0.5						,     output_type='''np'''						,     )

								UpperCAmelCase					:			Tuple									=						output.images[0]

								assert image.shape == (512, 512, 3)

								assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_						,     SCREAMING_SNAKE_CASE_						)










import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
	import torch

	from transformers import (
	    MraForMaskedLM,
	    MraForMultipleChoice,
	    MraForQuestionAnswering,
	    MraForSequenceClassification,
	    MraForTokenClassification,
	    MraModel,
	)
	from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=8,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=5,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=36,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=None,   )       ->   str:
						UpperCAmelCase_:		int							      =     parent
						UpperCAmelCase_:		Dict							      =     batch_size
						UpperCAmelCase_:		Optional[int]							      =     seq_length
						UpperCAmelCase_:		int							      =     is_training
						UpperCAmelCase_:		List[Any]							      =     use_input_mask
						UpperCAmelCase_:		int							      =     use_token_type_ids
						UpperCAmelCase_:		Tuple							      =     use_labels
						UpperCAmelCase_:		Tuple							      =     vocab_size
						UpperCAmelCase_:		int							      =     hidden_size
						UpperCAmelCase_:		List[str]							      =     num_hidden_layers
						UpperCAmelCase_:		List[str]							      =     num_attention_heads
						UpperCAmelCase_:		Any							      =     intermediate_size
						UpperCAmelCase_:		str							      =     hidden_act
						UpperCAmelCase_:		Optional[int]							      =     hidden_dropout_prob
						UpperCAmelCase_:		Optional[Any]							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		List[str]							      =     max_position_embeddings
						UpperCAmelCase_:		Optional[int]							      =     type_vocab_size
						UpperCAmelCase_:		Tuple							      =     type_sequence_label_size
						UpperCAmelCase_:		Tuple							      =     initializer_range
						UpperCAmelCase_:		str							      =     num_labels
						UpperCAmelCase_:		str							      =     num_choices
						UpperCAmelCase_:		Optional[int]							      =     scope

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					)

						UpperCAmelCase_:		List[str]							      =     None
						if self.use_input_mask:
									UpperCAmelCase_:		Union[str, Any]							      =     random_attention_mask([self.batch_size, self.seq_length]					)

						UpperCAmelCase_:		List[str]							      =     None
						if self.use_token_type_ids:
									UpperCAmelCase_:		str							      =     ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size					)

						UpperCAmelCase_:		str							      =     None
						UpperCAmelCase_:		List[Any]							      =     None
						UpperCAmelCase_:		Tuple							      =     None
						if self.use_labels:
									UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size],   self.type_sequence_label_size					)
									UpperCAmelCase_:		str							      =     ids_tensor([self.batch_size, self.seq_length],   self.num_labels					)
									UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size],   self.num_choices					)

						UpperCAmelCase_:		List[Any]							      =     self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

			def      __snake_case       (self					)       ->   List[Any]:
						return MraConfig(
						    vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   type_vocab_size=self.type_vocab_size,   is_decoder=SCREAMING_SNAKE_CASE_,   initializer_range=self.initializer_range,   )

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Optional[Any]							      =     self.get_config()
						UpperCAmelCase_:		Dict							      =     300
						return config

			def      __snake_case       (self					)       ->   List[Any]:
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		Any							      =     self.prepare_config_and_inputs()

						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Any							      =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
						UpperCAmelCase_:		List[str]							      =     ids_tensor([self.batch_size, self.seq_length],   vocab_size=2					)

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[int]							      =     MraModel(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						UpperCAmelCase_:		Any							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     MraModel(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_,   encoder_attention_mask=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		List[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		int							      =     MraForMaskedLM(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		int							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						UpperCAmelCase_:		Any							      =     MraForQuestionAnswering(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		str							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   start_positions=SCREAMING_SNAKE_CASE_,   end_positions=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.start_logits.shape,   (self.batch_size, self.seq_length)					)
						self.parent.assertEqual(result.end_logits.shape,   (self.batch_size, self.seq_length)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						UpperCAmelCase_:		List[str]							      =     self.num_labels
						UpperCAmelCase_:		Any							      =     MraForSequenceClassification(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Tuple							      =     self.num_labels
						UpperCAmelCase_:		Any							      =     MraForTokenClassification(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.num_choices
						UpperCAmelCase_:		int							      =     MraForMultipleChoice(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Dict							      =     input_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[str]							      =     token_type_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[Any]							      =     input_mask.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices)					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		int							      =     self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		Any							      =     config_and_inputs
						UpperCAmelCase_:		Dict							      =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_torch
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			A      		=	False
			A      		=	False
			A      		=	False
			A      		=	False

			A      		=	()

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Dict							      =     MraModelTester(self					)
						UpperCAmelCase_:		List[Any]							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_,   hidden_size=37					)

			def      __snake_case       (self					)       ->   Tuple:
						self.config_tester.run_common_tests()

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		int							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									UpperCAmelCase_:		str							      =     type
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		str							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		str							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_					)

			@slow
			def      __snake_case       (self					)       ->   Any:
						for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_:		List[str]							      =     MraModel.from_pretrained(SCREAMING_SNAKE_CASE_					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)

			@unittest.skip(reason="""MRA does not output attentions"""					)
			def      __snake_case       (self					)       ->   List[str]:
						return







@require_torch
class 				_a				(   unittest.TestCase					):

			@slow
			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     MraModel.from_pretrained("""uw-madison/mra-base-512-4"""					)
						UpperCAmelCase_:		Optional[Any]							      =     torch.arange(256					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Optional[int]							      =     torch.Size((1, 256, 768)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     torch.tensor(
						    [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)

			@slow
			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		List[str]							      =     MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""					)
						UpperCAmelCase_:		Dict							      =     torch.arange(256					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Any							      =     50265

						UpperCAmelCase_:		Any							      =     torch.Size((1, 256, vocab_size)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Dict							      =     torch.tensor(
						    [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)

			@slow
			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Optional[int]							      =     MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""					)
						UpperCAmelCase_:		List[Any]							      =     torch.arange(4096					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Dict							      =     50265

						UpperCAmelCase_:		int							      =     torch.Size((1, 4096, vocab_size)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[Any]							      =     torch.tensor(
						    [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)






